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Big Data Analytics: Systems, Algorithms, Applications / by C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, L.M. Jenila Livingston.

By: Contributor(s): Material type: TextTextPublication details: Singapur: Springer, c 2019.Edition: 1st ed. 2019Description: XXVI, 412 p.: 174 illus., 108 illus. in color.; 27 cmISBN:
  • 9789811500947
  • 9789811500930
  • 9789811500954
  • 9789811500961
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 005.74 23 PRA-B 2019 790703
Online resources:
Contents:
Big Data -- Intelligent Systems -- Analytics Models for Data Science -- Big Data Tools - Hadoop Eco System -- Predictive Modeling for Unstructured Data -- Machine Learning Algorithms for Big Data -- Social Semantic Web Mining and Big Data Analytics -- Internet of Things (IoT) and Big Data Analytics -- Big Data Analytics for Financial and Services Banking -- Big Data Analytics Techniques in Capital Market Use Cases.
In: Springer eBooksSummary: This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 790703 (Browse shelf(Opens below)) 1 Available 790703
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 790704 (Browse shelf(Opens below)) 2 Available 790704
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 790705 (Browse shelf(Opens below)) 3 Available 790705
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 791499 (Browse shelf(Opens below)) 4 Available 791499
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 791500 (Browse shelf(Opens below)) 5 Available 791500
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 791501 (Browse shelf(Opens below)) 6 Available 791501
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 791502 (Browse shelf(Opens below)) 7 Available 791502
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 791503 (Browse shelf(Opens below)) 8 Available 791503
Books Books Faculty of Information Technology Book Cart Book 005.74 PRA-B 2019 791504 (Browse shelf(Opens below)) 9 Available 791504
Total holds: 0

Big Data -- Intelligent Systems -- Analytics Models for Data Science -- Big Data Tools - Hadoop Eco System -- Predictive Modeling for Unstructured Data -- Machine Learning Algorithms for Big Data -- Social Semantic Web Mining and Big Data Analytics -- Internet of Things (IoT) and Big Data Analytics -- Big Data Analytics for Financial and Services Banking -- Big Data Analytics Techniques in Capital Market Use Cases.

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

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